基于改进CASREL的水稻施肥知识图谱信息抽取研究

Knowledge Graph Information Extraction for Rice Fertilization Based on Improved CASREL

为实现水稻施肥知识图谱自动化构建,为后续构建水稻施肥决策系统提供基础,定义了水稻施肥体系数据结构并制作水稻施肥数据集,结合水稻施肥数据特点,添加单位标注器,并改进CASREL解码加入隐藏层,提出了基于RoBERTa-wwm编码+改进CASREL解码的信息抽取模型,同时针对编码与解码环节进行试验对比。结果表明,基于该模型的F1值达到91.86%,与对比模型相比有较为显著的提升。基于改进RoBERTa-wwm-CASREL的信息抽取模型能有效提高水稻施肥信息抽取效果,为水稻施肥知识图谱构建以及施肥决策系统提供基础。

In order to construct a rice fertilizer knowledge structure, based on the existing rice fertilizer unstructured data information, a rice fertilizer knowledge graph entity and relationship knowledge structure was proposed and designed, through which the existing rice fertilizer information in the network was stored in the knowledge graph as structured data;in order to extract a large amount of information to be stored in the knowledge graph, and at the same time, for the information extraction i.e., the existence of the overlapping triad problem, a rice fertilizer information extraction model based on RoBERTa-wwm coding + improved CASREL decoding was proposed, and the model was improved according to the characteristics of rice fertilizer data, and relevant experimental comparisons were conducted in coding and decoding, respectively. The results showed that the F1 value of this rice fertilizer information extraction model reached 91.86%, which was a significant improvement in extraction effect compared with the comparison model. Therefore, it can be concluded that the information extraction model based on the improved RoBERTa-wwm-CASERL can effectively improve the extraction effect of rice fertilizer information, which provided a basis for the next step of constructing rice fertilizer knowledge map and rice fertilizer decision system.